Overview

Dataset statistics

Number of variables5
Number of observations4079
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.1 MiB
Average record size in memory275.5 B

Variable types

Numeric2
Text3

Alerts

ID is uniformly distributedUniform
ID has unique valuesUnique

Reproduction

Analysis started2026-02-05 16:49:31.105180
Analysis finished2026-02-05 16:49:42.186718
Duration11.08 seconds
Software versionydata-profiling vv4.18.1
Download configurationconfig.json

Variables

ID
Real number (ℝ)

Uniform  Unique 

Distinct4079
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2040
Minimum1
Maximum4079
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size63.7 KiB
2026-02-05T22:19:42.227944image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile204.9
Q11020.5
median2040
Q33059.5
95-th percentile3875.1
Maximum4079
Range4078
Interquartile range (IQR)2039

Descriptive statistics

Standard deviation1177.6502
Coefficient of variation (CV)0.57727951
Kurtosis-1.2
Mean2040
Median Absolute Deviation (MAD)1020
Skewness0
Sum8321160
Variance1386860
MonotonicityStrictly increasing
2026-02-05T22:19:42.263275image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11
 
< 0.1%
27251
 
< 0.1%
27121
 
< 0.1%
27131
 
< 0.1%
27141
 
< 0.1%
27151
 
< 0.1%
27161
 
< 0.1%
27171
 
< 0.1%
27181
 
< 0.1%
27191
 
< 0.1%
Other values (4069)4069
99.8%
ValueCountFrequency (%)
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
101
< 0.1%
ValueCountFrequency (%)
40791
< 0.1%
40781
< 0.1%
40771
< 0.1%
40761
< 0.1%
40751
< 0.1%
40741
< 0.1%
40731
< 0.1%
40721
< 0.1%
40711
< 0.1%
40701
< 0.1%

Name
Text

Distinct4001
Distinct (%)98.1%
Missing0
Missing (%)0.0%
Memory size307.8 KiB
2026-02-05T22:19:42.339136image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length33
Median length27
Mean length8.5126256
Min length3

Characters and Unicode

Total characters34723
Distinct characters91
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3936 ?
Unique (%)96.5%

Sample

1st rowKabul
2nd rowQandahar
3rd rowHerat
4th rowMazar-e-Sharif
5th rowAmsterdam
ValueCountFrequency (%)
de81
 
1.6%
san62
 
1.2%
la25
 
0.5%
santa22
 
0.4%
são16
 
0.3%
del16
 
0.3%
city12
 
0.2%
do12
 
0.2%
el11
 
0.2%
saint10
 
0.2%
Other values (4319)4722
94.6%
2026-02-05T22:19:42.435849image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a5025
 
14.5%
n2423
 
7.0%
i2245
 
6.5%
o2192
 
6.3%
e2048
 
5.9%
r1963
 
5.7%
u1482
 
4.3%
l1371
 
3.9%
s1184
 
3.4%
t1161
 
3.3%
Other values (81)13629
39.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)34723
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a5025
 
14.5%
n2423
 
7.0%
i2245
 
6.5%
o2192
 
6.3%
e2048
 
5.9%
r1963
 
5.7%
u1482
 
4.3%
l1371
 
3.9%
s1184
 
3.4%
t1161
 
3.3%
Other values (81)13629
39.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)34723
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a5025
 
14.5%
n2423
 
7.0%
i2245
 
6.5%
o2192
 
6.3%
e2048
 
5.9%
r1963
 
5.7%
u1482
 
4.3%
l1371
 
3.9%
s1184
 
3.4%
t1161
 
3.3%
Other values (81)13629
39.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)34723
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a5025
 
14.5%
n2423
 
7.0%
i2245
 
6.5%
o2192
 
6.3%
e2048
 
5.9%
r1963
 
5.7%
u1482
 
4.3%
l1371
 
3.9%
s1184
 
3.4%
t1161
 
3.3%
Other values (81)13629
39.3%
Distinct233
Distinct (%)5.7%
Missing0
Missing (%)0.0%
Memory size271.1 KiB
2026-02-05T22:19:42.504487image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length7
Median length3
Mean length3.0676636
Min length3

Characters and Unicode

Total characters12513
Distinct characters30
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique85 ?
Unique (%)2.1%

Sample

1st rowAFG
2nd rowAFG
3rd rowAFG
4th rowAFG
5th rowNLD
ValueCountFrequency (%)
chn363
 
8.9%
ind302
 
7.4%
usa274
 
6.7%
bra250
 
6.1%
jpn248
 
6.1%
rus189
 
4.6%
mex171
 
4.2%
phl133
 
3.3%
deu93
 
2.3%
idn85
 
2.1%
Other values (223)1971
48.3%
2026-02-05T22:19:42.598798image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
N1432
 
11.4%
R1109
 
8.9%
A1093
 
8.7%
U879
 
7.0%
S684
 
5.5%
P622
 
5.0%
D608
 
4.9%
I594
 
4.7%
C591
 
4.7%
H587
 
4.7%
Other values (20)4314
34.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)12513
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N1432
 
11.4%
R1109
 
8.9%
A1093
 
8.7%
U879
 
7.0%
S684
 
5.5%
P622
 
5.0%
D608
 
4.9%
I594
 
4.7%
C591
 
4.7%
H587
 
4.7%
Other values (20)4314
34.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)12513
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N1432
 
11.4%
R1109
 
8.9%
A1093
 
8.7%
U879
 
7.0%
S684
 
5.5%
P622
 
5.0%
D608
 
4.9%
I594
 
4.7%
C591
 
4.7%
H587
 
4.7%
Other values (20)4314
34.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)12513
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N1432
 
11.4%
R1109
 
8.9%
A1093
 
8.7%
U879
 
7.0%
S684
 
5.5%
P622
 
5.0%
D608
 
4.9%
I594
 
4.7%
C591
 
4.7%
H587
 
4.7%
Other values (20)4314
34.5%
Distinct1352
Distinct (%)33.1%
Missing0
Missing (%)0.0%
Memory size308.5 KiB
2026-02-05T22:19:42.668748image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length20
Median length17
Mean length8.9823486
Min length1

Characters and Unicode

Total characters36639
Distinct characters90
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique809 ?
Unique (%)19.8%

Sample

1st rowKabol
2nd rowQandahar
3rd rowHerat
4th rowBalkh
5th rowNoord-Holland
ValueCountFrequency (%)
pradesh96
 
1.8%
west83
 
1.6%
unknown73
 
1.4%
california73
 
1.4%
são70
 
1.3%
england70
 
1.3%
paulo69
 
1.3%
central61
 
1.2%
51
 
1.0%
java49
 
0.9%
Other values (1457)4575
86.8%
2026-02-05T22:19:42.771706image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a5486
 
15.0%
n2987
 
8.2%
i2449
 
6.7%
o2308
 
6.3%
e2008
 
5.5%
r1997
 
5.5%
s1513
 
4.1%
t1376
 
3.8%
l1367
 
3.7%
u1237
 
3.4%
Other values (80)13911
38.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)36639
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a5486
 
15.0%
n2987
 
8.2%
i2449
 
6.7%
o2308
 
6.3%
e2008
 
5.5%
r1997
 
5.5%
s1513
 
4.1%
t1376
 
3.8%
l1367
 
3.7%
u1237
 
3.4%
Other values (80)13911
38.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)36639
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a5486
 
15.0%
n2987
 
8.2%
i2449
 
6.7%
o2308
 
6.3%
e2008
 
5.5%
r1997
 
5.5%
s1513
 
4.1%
t1376
 
3.8%
l1367
 
3.7%
u1237
 
3.4%
Other values (80)13911
38.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)36639
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a5486
 
15.0%
n2987
 
8.2%
i2449
 
6.7%
o2308
 
6.3%
e2008
 
5.5%
r1997
 
5.5%
s1513
 
4.1%
t1376
 
3.8%
l1367
 
3.7%
u1237
 
3.4%
Other values (80)13911
38.0%

Population
Real number (ℝ)

Distinct3833
Distinct (%)94.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean342663.99
Minimum42
Maximum9981619
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size63.7 KiB
2026-02-05T22:19:42.802646image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum42
5-th percentile91698.4
Q1115455
median167051
Q3304288
95-th percentile1097146.1
Maximum9981619
Range9981577
Interquartile range (IQR)188833

Descriptive statistics

Standard deviation690721.99
Coefficient of variation (CV)2.0157414
Kurtosis81.157231
Mean342663.99
Median Absolute Deviation (MAD)63947
Skewness7.9082322
Sum1.3977264 × 109
Variance4.7709687 × 1011
MonotonicityNot monotonic
2026-02-05T22:19:42.835998image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16705173
 
1.8%
9000012
 
0.3%
1010006
 
0.1%
1300004
 
0.1%
1270004
 
0.1%
1033004
 
0.1%
923004
 
0.1%
920004
 
0.1%
1000004
 
0.1%
1408004
 
0.1%
Other values (3823)3960
97.1%
ValueCountFrequency (%)
421
< 0.1%
1671
< 0.1%
3001
< 0.1%
4551
< 0.1%
5031
< 0.1%
5591
< 0.1%
5951
< 0.1%
6821
< 0.1%
7001
< 0.1%
8001
< 0.1%
ValueCountFrequency (%)
99816191
< 0.1%
99684851
< 0.1%
96963001
< 0.1%
96049001
< 0.1%
92692651
< 0.1%
87879581
< 0.1%
85913091
< 0.1%
80082781
< 0.1%
79802301
< 0.1%
74720001
< 0.1%

Interactions

2026-02-05T22:19:39.248280image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-05T22:19:31.246922image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-05T22:19:42.102043image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-05T22:19:36.371040image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2026-02-05T22:19:42.856825image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
IDPopulation
ID1.000-0.031
Population-0.0311.000

Missing values

2026-02-05T22:19:42.138123image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2026-02-05T22:19:42.159021image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

IDNameCountryCodeDistrictPopulation
01KabulAFGKabol1780000.0
12QandaharAFGQandahar237500.0
23HeratAFGHerat186800.0
34Mazar-e-SharifAFGBalkh127800.0
45AmsterdamNLDNoord-Holland731200.0
56RotterdamNLDZuid-Holland593321.0
67HaagNLDZuid-Holland440900.0
78UtrechtNLDUtrecht234323.0
89EindhovenNLDNoord-Brabant201843.0
910TilburgNLDNoord-Brabant193238.0
IDNameCountryCodeDistrictPopulation
40704070ChitungwizaZWEHarare274912.0
40714071Mount DarwinZWEHarare164362.0
40724072MutareZWEManicaland131367.0
40734073GweruZWEMidlands128037.0
40744074GazaPSEGaza353632.0
40754075Khan YunisPSEKhan Yunis123175.0
40764076HebronPSEHebron119401.0
40774077JabaliyaPSENorth Gaza113901.0
40784078NablusPSENablus100231.0
40794079RafahPSERafah92020.0